A NON-HOMOGENEOUS SEMI-MARKOV REWARD MODEL FOR THE CREDIT SPREAD COMPUTATION

2011 ◽  
Vol 14 (02) ◽  
pp. 221-238 ◽  
Author(s):  
GUGLIELMO D'AMICO ◽  
JACQUES JANSSEN ◽  
RAIMONDO MANCA
Keyword(s):  
Interest Rates ◽  
Credit Spread ◽  
Credit Spreads ◽  
Time Interval ◽  
Yield Spread ◽  
Migration Process ◽  
Discount Factors ◽  
Semi Markov Process ◽  
Markov Reward ◽  
Markov Reward Model

In this paper, we present a model to describe the evolution of the yield spread by considering the rating evaluation as the determinant of credit spreads. The underlying rating migration process is assumed to be a non-homogeneous discrete time semi-Markov process. We calculate the total sum of mean basis points paid within any given time interval. From this information we show how it is possible to extract the time evolution of expected interest rates and discount factors.

Rating Agency Adjustments to GAAP Financial Statements and Their Effect on Ratings and Credit Spreads

2014 ◽  
Vol 90 (2) ◽  
pp. 641-674 ◽  
Author(s):  
Pepa Kraft
Keyword(s):  
Default Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Balance Sheet ◽  
Financial Statements ◽  
Financial Statement ◽  
Credit Spread ◽  
Credit Spreads ◽  
Term Structures ◽  
Interest Expense

ABSTRACT I examine a dataset of both quantitative (hard) adjustments to firms' reported U.S. GAAP financial statement numbers and qualitative (soft) adjustments to firms' credit ratings that Moody's develops and uses in its credit rating process. I first document differences between firms' reported and Moody's adjusted numbers that are both large and frequent across firms. For example, primarily because of upward adjustments to interest expense and debt attributable to firms' off-balance sheet debt, on average, adjusted coverage (cash flow-to-debt) ratios are 27 percent (8 percent) lower and adjusted leverage ratios are 70 percent higher than the corresponding U.S. GAAP ratios. I then find that Moody's hard and soft rating adjustments are associated with significantly higher credit spreads and flatter credit spread term structures. Overall, the results indicate that Moody's quantitative adjustments to financial statement numbers and qualitative adjustments to credit ratings enable it to better capture default risk, consistent with it effectively processing both hard and soft information.

Partially observable semi-Markov reward processes

1993 ◽  
Vol 30 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Yasushi Masuda
Keyword(s):  
Markov Process ◽  
Counting Process ◽  
Probabilistic Interpretation ◽  
Real Domain ◽  
Semi Markov Process ◽  
Reward Process ◽  
Markov Reward ◽  
Partially Observable ◽  
Joint Behavior

The main objective of this paper is to investigate the conditional behavior of the multivariate reward process given the number of certain signals where the underlying system is described by a semi-Markov process and the signal is defined by a counting process. To this end, we study the joint behavior of the multivariate reward process and the multivariate counting process in detail. We derive transform results as well as the corresponding real domain expressions, thus providing clear probabilistic interpretation.

scholarly journals Risks and the influence of negative interest rates on economic activity: a case study of Sweden, Denmark, and Switzerland

2020 ◽  
Vol 15 (1) ◽  
pp. 30-41
Author(s):  
Liběna Černohorská ◽  
Darina Kubicová
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Granger Causality ◽  
Economic Activity ◽  
Central Banks ◽  
Time Interval ◽  
Short Term ◽  
Stock Indices ◽  
Negative Interest Rates ◽  
The Impact

The purpose of this paper is to analyze the impact of negative interest rates on economic activity in a selected group of countries, in particular Sweden, Denmark, and Switzerland, for the period 2009–2018. The central banks of these countries were among the first to implement negative interest rates to revive the economic growth. Therefore, this study analyzed long- and short-term relationships between interest rates announced by central banks and gross domestic product and blue chip stock indices. Time series analysis was conducted using Engle-Granger cointegration analysis and Granger causality testing to identify long- and short-term relationship. The first step, using the Akaike criteria, was to determine the optimal delay of the entire time interval for the analyzed periods. Time series that seem to be stationary were excluded based on the results of the Dickey-Fuller test. Further testing continued with the Engle-Granger test if the conditions were met. It was designed to identify co-integration relationships that would show correlation between the selected variables. These tests showed that at a significance level of 0.05, there is no co-integration between any time series in the countries analyzed. On the basis of these analyses, it was determined that there were no long-term relationships between interest rates and GDP or stock indices for these countries during the monitored time period. Using Granger causality, the study only confirmed short-term relationship between interest rates and GDP for all examined countries, though not between interest rates and the stock indices. Acknowledgment The paper has been created with the financial support of The Czech Science Foundation GACR 18-05244S – Innovative Approaches to Credit Risk Management.

A Symbolic Approach to the Analysis of Multi-Formalism Markov Reward Models

2013 ◽  
pp. 170-195
Author(s):  
Kai Lampka ◽  
Markus Siegle
Keyword(s):  
Functional System ◽  
State Variables ◽  
State Charts ◽  
Level Model ◽  
System Properties ◽  
Markov Reward ◽  
High Level ◽  
Symbolic Approach ◽  
State Space Generation ◽  
Markov Reward Model

When modelling large systems, modularity is an important concept, as it aids modellers to master the complexity of their model. Moreover, employing different modelling formalisms within the same modelling project has the potential to ease the description of various parts or aspects of the overall system. In the area of performability modelling, formalisms such as stochastic reward nets, stochastic process algebras, stochastic automata, or stochastic UML state charts are often used, and several of these may be employed within one modelling project. This chapter presents an approach for efficiently constructing a symbolic representation in the form of a zero-suppressed Binary Decision Diagram (BDD), which represents the Markov Reward Model underlying a multi-formalism high-level model. In this approach, the interaction between the submodels may be established either by the sharing of state variables or by the synchronisation of common activities. It is shown that the Decision Diagram data structure and the associated algorithms enable highly efficient state space generation and different forms of analysis of the underlying Markov Reward Model (e.g. calculation of reward measures or asserting non-functional system properties by means of model checking techniques).

THE IMPACT OF LEARNING ON BUSINESS CYCLE FLUCTUATIONS IN THE CONSUMER UNSECURED CREDIT MARKET

2018 ◽  
Vol 24 (5) ◽  
pp. 1087-1123 ◽  
Author(s):  
Matthew N. Luzzetti ◽  
Seth Neumuller
Keyword(s):  
Business Cycle ◽  
Interest Rates ◽  
Default Risk ◽  
Credit Market ◽  
Credit Spread ◽  
Cross Sectional ◽  
Business Cycle Fluctuations ◽  
Unsecured Debt ◽  
Consumer Default ◽  
The Impact

We document that the credit spread on consumer unsecured debt exhibits a persistent, hump-shaped response to an increase in the charge-off rate. This stylized fact poses a significant challenge for a standard model of consumer default in which lenders have rational expectations and, therefore, the credit spread continuously adjusts to reflect the true default incentives of each borrower. In an effort to explain this feature of the data, we construct a model of consumer default with countercyclical income risk in which lenders learn about default risk over time by observing the history of repayment decisions, as is the case in practice. In addition to matching credit spread dynamics, allowing lenders to learn about default risk substantially improves the model’s ability to generate realistic business cycle fluctuations in the consumer unsecured credit market and match the cross-sectional distribution of unsecured debt and dispersion of interest rates observed in the data.

Partially observable semi-Markov reward processes

1993 ◽  
Vol 30 (03) ◽  
pp. 548-560 ◽  
Author(s):  
Yasushi Masuda
Keyword(s):  
Markov Process ◽  
Counting Process ◽  
Probabilistic Interpretation ◽  
Real Domain ◽  
Semi Markov Process ◽  
Reward Process ◽  
Markov Reward ◽  
Partially Observable ◽  
Joint Behavior

The main objective of this paper is to investigate the conditional behavior of the multivariate reward process given the number of certain signals where the underlying system is described by a semi-Markov process and the signal is defined by a counting process. To this end, we study the joint behavior of the multivariate reward process and the multivariate counting process in detail. We derive transform results as well as the corresponding real domain expressions, thus providing clear probabilistic interpretation.

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